9results about How to "High quality data" patented technology

The invention discloses a land-based data automatic receiving and preprocessing management system and method, and relates to the technical field of ocean observation data processing, and the system comprises a communication service subsystem, a data preprocessing subsystem, an intelligent perception linkage subsystem, and a data management subsystem. And full-process automatic processing of underwater acoustic real-time communication subsurface buoy observation data is realized through modular integration. Automatic receiving, safe storage and efficient preprocessing of deep sea power and biogeochemical element data are achieved, reliable data support is provided for marine scientific research and environment monitoring, and the long-term observation requirement of a 6000-meter water depth subsurface buoy system is met.

The invention discloses a joint surgical operation data fusion processing method and system based on a multi-modal image, and relates to the technical field of image processing. The method comprises the following steps: performing motion state evaluation based on joint trajectory data of each posture point image in a multi-modal image, and dynamically acquiring a reference joint image of the multi-modal image based on a motion state evaluation result; performing image accuracy evaluation based on joint position data in each reference joint image, and dynamically correcting the joint data based on an image accuracy evaluation result; and forming a joint three-dimensional model based on the corrected joint data, performing dynamic region division based on joint evaluation parameters in the joint three-dimensional model, and dynamically adjusting the model geometric accuracy of the joint three-dimensional model based on a region division result and a joint category. Accurate quantitative analysis of the joint movement function, individualized high-precision three-dimensional model reconstruction and systematic improvement of clinical diagnosis efficiency are achieved.

A method for determining the association and continuity combination of top-side-rock structural surfaces based on unsupervised learning is proposed. This method involves investigating the structural surfaces of the roof and side-rock masses in underground engineering projects to obtain geometric and statistical information. A unified encoding is performed to construct a joint feature set of roof-side-rock structural surfaces. An unsupervised density-based clustering algorithm is used for initial cluster analysis of the joint feature set. A swarm intelligence-based Lüperfox optimization algorithm is introduced to optimize key parameters of the DBSCAN clustering algorithm. A Latin hypercube sampling method is used to improve the population initialization process of the Lüperfox optimization algorithm, resulting in better DBSCAN hyperparameters and optimized structural surface clustering results. The method determines the structural surface system affiliation of structural surfaces in different locations, quantitatively calculates the spatial continuity and combination relationship of structural surfaces, and outputs the spatial association determination results and continuity levels of the roof-side-rock structural surfaces. This method enables intelligent identification and quantitative analysis of the spatial relationship between roof and side-rock rock structural surfaces.

The invention relates to the technical field of image data processing, in particular to a CT image feature extraction method for gastrointestinal stromal tumor risk stratification, which comprises the following steps of: firstly, acquiring an abdominal CT plane scanning period image and a venous period image of a gastrointestinal stromal tumor patient, and performing coarse segmentation by utilizing gray scale difference of the two periods to obtain a tumor suspected area; a suspected content area is identified by analyzing gray change characteristics of the edge of the area, whether the suspected content area is a real gastrointestinal tract content (such as a contrast agent or bubbles) is further verified, the confirmed content is removed, a pure gastrointestinal stromal tumor area is finally obtained, and radiomics characteristics for risk stratification are extracted from the pure gastrointestinal stromal tumor area. According to the scheme, interference of gastrointestinal tract contents on tumor boundary judgment and internal texture analysis is effectively eliminated, so that the segmentation result is more accurate, high-quality data is provided for image omics feature extraction, the accuracy and reliability of risk layering are enhanced, and preoperative noninvasive accurate evaluation is realized.

The present application relates to the technical field of biology, in particular to human thymus tissue digestion solution and application thereof. The present application provides a preparation method of human thymus tissue digestion solution and human thymus tissue single cells, and the preparation method can obtain a human thymus tissue primary cell suspension with high viability, low fine nodule rate, high nucleated rate and less impurities, and can greatly improve the success rate of single cell sequencing experiment of human thymus tissue primary cells.

The present application relates to the plant hospital intelligent management system based on disease identification model, relates to rice processing technical field, including multi-source data sensing unit, feature extraction unit, fusion research unit, decision checking unit and artificial auxiliary decision unit;The present application simulates the logic of expert comprehensive research and judgment through multi-source data fusion, can effectively identify the same disease, utilizes environmental constraint, greatly reduces the misdiagnosis rate and the missed diagnosis rate, simultaneously introduces standard image and historical disease image collaborative modeling, through multi-scale feature extraction, enhances the capture ability of the disease representation under the complex background of the tiny disease spot, provides high-purity feature support for subsequent research and judgment, through the refinement management of the professional degree level and the treatment direction of the person who sees a doctor, realizes the accurate automatic matching of difficult disease and experienced experts, ensures that the complex case can obtain the most professional audit, trains the prior model through the historical data, and finally corrects the diagnosis result by artificial opinion.

The invention discloses a non-contact bank card POS transaction real-time risk control system and risk control method based on rule and MLP neural network collaboration, and the method comprises the steps: presetting a data collection point at a bottom layer of an EMV transaction kernel, and obtaining the time sequence behavior data of a transaction process; constructing a multi-dimensional risk feature vector based on the data; performing parallel analysis by using an MLP model deployed in a terminal security area and a rule engine; outputting a final risk control instruction through a preset decision fusion strategy; meanwhile, an end-cloud collaborative data closed loop is established, data collected by a terminal is transmitted to a cloud end, continuous training and model parameter updating of a model are supported, millisecond risk interception in a non-network connection or high-delay sensitive scene is achieved, the dynamic adaptive capacity to a novel fraud mode is improved through a continuous evolution mechanism, and the risk interception efficiency is improved. The technical defects that a traditional risk control scheme is high in response delay and insufficient in terminal side defense capability are effectively overcome.

The invention relates to the technical field of power systems, and discloses a load sequence generation method, system and device and a medium. The method comprises the following steps: dividing an obtained original load sequence into a first reference load sequence and a first dynamic load sequence through screening and clustering; combining a joint probability distribution model and a Markov transfer matrix to reconstruct a daily load curve of a simulated typical day, and generating a second reference load sequence; inputting the screened target external factors influencing the first dynamic load sequence into a neural network model fusing a bidirectional time sequence convolutional network, a gating circulation unit and a double-stage attention mechanism to carry out load prediction, and generating a second dynamic load sequence; and correspondingly superposing the second reference load sequence and the second dynamic load sequence according to the time dimension to obtain a target load sequence. According to the invention, high-quality load simulation data support is provided for refined operation of a power system.

The invention discloses a pumped storage ultrasonic flowmeter temperature compensation model and method based on LSTM (Long Short Term Memory), and aims to overcome the defect that an existing temperature compensation method is difficult to process nonlinear and dynamic temperature influences under complex working conditions of a pumped storage power plant. According to the method, temperature, ultrasonic propagation time and flow data under laboratory simulation and on-site actual working conditions are collected, and after cleaning and standardized preprocessing, a neural network model comprising an input layer, an LSTM layer, a full-connection layer and an output layer is constructed and trained. The model can learn a long-term time sequence dependency relationship between the temperature and the flow error, outputs a high-precision error compensation value, and is deployed on a main control chip of the flow meter to realize real-time dynamic compensation. The measurement precision and adaptability of the ultrasonic flowmeter under the wide temperature range condition are remarkably improved, and reliable support is provided for precise operation of a pumped storage power plant.